Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Embodiments relate to data visualization techniques, and in particular, to methods and apparatuses providing copying of data view portions relevant for analysis and/or collaboration.
Relational databases offer a useful mechanism for storing and analyzing volumes of related data. One common form of database storage is as a table comprising rows and columns.
The increasing power of databases has resulted in the formation of database tables of ever-greater size and complexity. Data volumes comprising millions or an even a larger number of records, may commonly be accessible utilizing database technologies.
Typically only a relatively small subset of database data may actually be relevant to any particular analysis and/or collaboration task. Conventionally, however, formatting larger data sets to highlight subsets thereof, requires a user to perform a number of manual steps. For example the user may need to manipulate and/or delete non-relevant columns, rows, and cells etc., a potentially time-consuming task.
And, even where the user does manage to manually select a relevant data portion and separate it from the larger visualization, the context of the selection may not be readily apparent. Similarly, when the user seeks to collaborate with others regarding a table or other data visualization, the subject of discussion is generally not the entire data set, but rather a limited subset of particular importance.
Accurate, insightful analysis of database data may thus depend upon a user being able to highlight a few important items. Where, however, large volumes of data are being stored in the database, that critical data can become obscured or masked within the mass of data, and its significance lost on an analyst and/or collaborator.